发表机构
Masaryk University; CESNET（马萨里克大学; CESNET）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了星团成员判定方法，比较了主要方法的统计假设，并通过案例研究揭示方法选择导致成员列表差异，强调不同定义和参数对结果的影响。
AI 中文摘要
可靠的恒星团成员关系对于推导星团的年龄、距离、红化、金属丰度、质量函数、动力学参数以及银河系结构示踪体至关重要。Gaia 时代产生了大量成员判定和星团探测方法，但由此得到的星表之间并不具有互换性。我们回顾了成员赋值的历史发展，比较了主要方法的统计假设，并明确考察了方法论选择对真实 Gaia 数据的实际后果。我们考虑了空间、自行、最大似然、测光、UPMASK、基于密度、机器学习、贝叶斯和自助法（bootstrap）成员判定方法。我们增加了三个案例研究：对 CantatGaudinAnders2020、HuntReffert2023 和 Perren2023 在共同星团上的直接比较；昴宿星团（Pleiades）的天测比较；以及对统一星团星表（UCC）中低信任候选体的批判性检验，包括 CWNU、CWWDL 和 CKCWDM 天体。文献表明，分歧并非仅仅是 Gaia 测量误差的结果。对星团的不同定义、不同的星等极限、对不确定性的不同处理、不同的空间先验以及对延展或低密度星族的不同容忍度，都可能导致显著不同的成员列表。Perren2023 报告称，在亮星等下，其成员列表与 CantatGaudinAnders2020 的平均重叠率约为 75%–80%，与 HuntReffert2023 的平均重叠率为 70%–75%，而 HUNT23 的重叠率在 G=20 时降至约 35%。UCC 还将许多新报告的候选体标记为低信任天体，当它们的成员分布稀疏、文献支持薄弱或存在强烈的重复/非星团指标时。
英文摘要
Reliable stellar-cluster membership is essential for deriving cluster ages, distances, reddenings, metallicities, mass functions, dynamical parameters, and Galactic-structure tracers. The Gaia era has produced a proliferation of membership and cluster-detection methods, but the resulting catalogues are not interchangeable. We review the historical development of membership assignment, compare the statistical assumptions of major methods, and explicitly examine the practical consequences of methodological choices on real Gaia data. We consider spatial, proper-motion, maximum-likelihood, photometric, UPMASK, density-based, machine-learning, Bayesian, and bootstrap membership approaches. We add three case studies: a direct comparison of \citet{CantatGaudinAnders2020}, \citet{HuntReffert2023}, and \citet{Perren2023} for common clusters; a Pleiades astrometric comparison; and a critical examination of low-trust candidates in the Unified Cluster Catalogue (UCC), including CWNU, CWWDL, and CKCWDM objects. The literature shows that disagreement is not simply a consequence of Gaia measurement errors. Different definitions of a cluster, different magnitude limits, different treatment of uncertainties, different spatial priors, and different tolerances for extended or low-density populations can lead to substantially different member lists. \citet{Perren2023} reported an average member-list overlap of roughly 75--80\% with \citet{CantatGaudinAnders2020} and 70--75\% with \citet{HuntReffert2023} at bright magnitudes, with the HUNT23 overlap decreasing to about 35\% at $G=20$. The UCC also flags many newly reported candidates as low-trust objects when their member distribution is sparse, their literature support is weak, or they have strong duplicate/non-cluster indicators.
Comments10 pages; 5 figure; 5 tables; submitted to A&A
